{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "学习目标\n",
    "- 应用groupby和聚合函数实现数据的分组与聚合"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "f1e8e1be29f9546b"
  },
  {
   "cell_type": "markdown",
   "source": [
    "分组与聚合通常是分析数据的一种方式，通常与一些统计函数一起使用，查看数据的分组情况\n",
    "# 1 什么分组与聚合\n",
    "![](../.images/分组聚合原理.png)"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "eef388276b98d6fd"
  },
  {
   "cell_type": "markdown",
   "source": [
    "# 2 分组API\n",
    "- DataFrame.groupby(key, as_index=False)\n",
    "    - key:分组的列数据，可以多个\n",
    "- 案例:不同颜色的不同笔的价格数据"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "95b1f3c266e15027"
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "   color   object  price1  price2\n0  white      pen    5.56    4.75\n1    red   pencil    4.20    4.12\n2  green   pencil    1.30    1.60\n3    red  ashtray    0.56    0.75\n4  green      pen    2.75    3.15",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>color</th>\n      <th>object</th>\n      <th>price1</th>\n      <th>price2</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>white</td>\n      <td>pen</td>\n      <td>5.56</td>\n      <td>4.75</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>red</td>\n      <td>pencil</td>\n      <td>4.20</td>\n      <td>4.12</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>green</td>\n      <td>pencil</td>\n      <td>1.30</td>\n      <td>1.60</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>red</td>\n      <td>ashtray</td>\n      <td>0.56</td>\n      <td>0.75</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>green</td>\n      <td>pen</td>\n      <td>2.75</td>\n      <td>3.15</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "col =pd.DataFrame({'color': ['white','red','green','red','green'], 'object': ['pen','pencil','pencil','ashtray','pen'],'price1':[5.56,4.20,1.30,0.56,2.75],'price2':[4.75,4.12,1.60,0.75,3.15]})\n",
    "col"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-22T11:17:46.978578700Z",
     "start_time": "2024-02-22T11:17:46.974211500Z"
    }
   },
   "id": "37da1e4d2b49e890",
   "execution_count": 3
  },
  {
   "cell_type": "markdown",
   "source": [
    "- 进行分组，对颜色分组，price进行聚合\n"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "6d85d4d37b5dc10d"
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "color\ngreen    2.025\nred      2.380\nwhite    5.560\nName: price1, dtype: float64"
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 分组，求平均值\n",
    "col.groupby(['color'])['price1'].mean()\n",
    "col['price1'].groupby(col['color']).mean()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-22T11:24:10.702640800Z",
     "start_time": "2024-02-22T11:24:10.696216700Z"
    }
   },
   "id": "914a7e9165d9dd83",
   "execution_count": 5
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "   color  price1\n0  green   2.025\n1    red   2.380\n2  white   5.560",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>color</th>\n      <th>price1</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>green</td>\n      <td>2.025</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>red</td>\n      <td>2.380</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>white</td>\n      <td>5.560</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# 分组，数据的结构不变\n",
    "col.groupby(['color'], as_index=False)['price1'].mean()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-22T11:24:12.781581900Z",
     "start_time": "2024-02-22T11:24:12.775548700Z"
    }
   },
   "id": "99d193d14769baf1",
   "execution_count": 6
  },
  {
   "cell_type": "markdown",
   "source": [
    "# 3 星巴克零售店铺数据\n",
    "## 3.1 数据获取\n"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "2f17f2ef608950b0"
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "           Brand  Store Number        Store Name Ownership Type  \\\n0      Starbucks  47370-257954     Meritxell, 96       Licensed   \n1      Starbucks  22331-212325  Ajman Drive Thru       Licensed   \n2      Starbucks  47089-256771         Dana Mall       Licensed   \n3      Starbucks  22126-218024        Twofour 54       Licensed   \n4      Starbucks  17127-178586      Al Ain Tower       Licensed   \n...          ...           ...               ...            ...   \n25595  Starbucks  21401-212072               Rex       Licensed   \n25596  Starbucks  24010-226985          Panorama       Licensed   \n25597  Starbucks  47608-253804     Rosebank Mall       Licensed   \n25598  Starbucks  47640-253809      Menlyn Maine       Licensed   \n25599  Starbucks  47609-253286    Mall of Africa       Licensed   \n\n                                          Street Address  \\\n0                                      Av. Meritxell, 96   \n1                                   1 Street 69, Al Jarf   \n2                           Sheikh Khalifa Bin Zayed St.   \n3                                        Al Salam Street   \n4                        Khaldiya Area, Abu Dhabi Island   \n...                                                  ...   \n25595  141 Nguyễn Huệ, Quận 1, Góc đường Pasteur và L...   \n25596  SN-44, Tòa Nhà Panorama, 208 Trần Văn Trà, Quận 7   \n25597          Cnr Tyrwhitt and Cradock Avenue, Rosebank   \n25598  Shop 61B, Central Square, Cnr Aramist & Coroba...   \n25599             Shop 2077, Upper Level, Waterfall City   \n\n                        City State/Province Country Postcode  Phone Number  \\\n0           Andorra la Vella              7      AD    AD500     376818720   \n1                      Ajman             AJ      AE      NaN           NaN   \n2                      Ajman             AJ      AE      NaN           NaN   \n3                  Abu Dhabi             AZ      AE      NaN           NaN   \n4                  Abu Dhabi             AZ      AE      NaN           NaN   \n...                      ...            ...     ...      ...           ...   \n25595  Thành Phố Hồ Chí Minh             SG      VN    70000  08 3824 4668   \n25596  Thành Phố Hồ Chí Minh             SG      VN    70000  08 5413 8292   \n25597           Johannesburg             GT      ZA     2194   27873500159   \n25598                 Menlyn             GT      ZA      181           NaN   \n25599                Midrand             GT      ZA     1682   27873500215   \n\n                             Timezone  Longitude  Latitude  \n0             GMT+1:00 Europe/Andorra       1.53     42.51  \n1                GMT+04:00 Asia/Dubai      55.47     25.42  \n2                GMT+04:00 Asia/Dubai      55.47     25.39  \n3                GMT+04:00 Asia/Dubai      54.38     24.48  \n4                GMT+04:00 Asia/Dubai      54.54     24.51  \n...                               ...        ...       ...  \n25595          GMT+000000 Asia/Saigon     106.70     10.78  \n25596          GMT+000000 Asia/Saigon     106.71     10.72  \n25597  GMT+000000 Africa/Johannesburg      28.04    -26.15  \n25598  GMT+000000 Africa/Johannesburg      28.28    -25.79  \n25599  GMT+000000 Africa/Johannesburg      28.11    -26.02  \n\n[25600 rows x 13 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Brand</th>\n      <th>Store Number</th>\n      <th>Store Name</th>\n      <th>Ownership Type</th>\n      <th>Street Address</th>\n      <th>City</th>\n      <th>State/Province</th>\n      <th>Country</th>\n      <th>Postcode</th>\n      <th>Phone Number</th>\n      <th>Timezone</th>\n      <th>Longitude</th>\n      <th>Latitude</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Starbucks</td>\n      <td>47370-257954</td>\n      <td>Meritxell, 96</td>\n      <td>Licensed</td>\n      <td>Av. Meritxell, 96</td>\n      <td>Andorra la Vella</td>\n      <td>7</td>\n      <td>AD</td>\n      <td>AD500</td>\n      <td>376818720</td>\n      <td>GMT+1:00 Europe/Andorra</td>\n      <td>1.53</td>\n      <td>42.51</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Starbucks</td>\n      <td>22331-212325</td>\n      <td>Ajman Drive Thru</td>\n      <td>Licensed</td>\n      <td>1 Street 69, Al Jarf</td>\n      <td>Ajman</td>\n      <td>AJ</td>\n      <td>AE</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>GMT+04:00 Asia/Dubai</td>\n      <td>55.47</td>\n      <td>25.42</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Starbucks</td>\n      <td>47089-256771</td>\n      <td>Dana Mall</td>\n      <td>Licensed</td>\n      <td>Sheikh Khalifa Bin Zayed St.</td>\n      <td>Ajman</td>\n      <td>AJ</td>\n      <td>AE</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>GMT+04:00 Asia/Dubai</td>\n      <td>55.47</td>\n      <td>25.39</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Starbucks</td>\n      <td>22126-218024</td>\n      <td>Twofour 54</td>\n      <td>Licensed</td>\n      <td>Al Salam Street</td>\n      <td>Abu Dhabi</td>\n      <td>AZ</td>\n      <td>AE</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>GMT+04:00 Asia/Dubai</td>\n      <td>54.38</td>\n      <td>24.48</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Starbucks</td>\n      <td>17127-178586</td>\n      <td>Al Ain Tower</td>\n      <td>Licensed</td>\n      <td>Khaldiya Area, Abu Dhabi Island</td>\n      <td>Abu Dhabi</td>\n      <td>AZ</td>\n      <td>AE</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>GMT+04:00 Asia/Dubai</td>\n      <td>54.54</td>\n      <td>24.51</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>25595</th>\n      <td>Starbucks</td>\n      <td>21401-212072</td>\n      <td>Rex</td>\n      <td>Licensed</td>\n      <td>141 Nguyễn Huệ, Quận 1, Góc đường Pasteur và L...</td>\n      <td>Thành Phố Hồ Chí Minh</td>\n      <td>SG</td>\n      <td>VN</td>\n      <td>70000</td>\n      <td>08 3824 4668</td>\n      <td>GMT+000000 Asia/Saigon</td>\n      <td>106.70</td>\n      <td>10.78</td>\n    </tr>\n    <tr>\n      <th>25596</th>\n      <td>Starbucks</td>\n      <td>24010-226985</td>\n      <td>Panorama</td>\n      <td>Licensed</td>\n      <td>SN-44, Tòa Nhà Panorama, 208 Trần Văn Trà, Quận 7</td>\n      <td>Thành Phố Hồ Chí Minh</td>\n      <td>SG</td>\n      <td>VN</td>\n      <td>70000</td>\n      <td>08 5413 8292</td>\n      <td>GMT+000000 Asia/Saigon</td>\n      <td>106.71</td>\n      <td>10.72</td>\n    </tr>\n    <tr>\n      <th>25597</th>\n      <td>Starbucks</td>\n      <td>47608-253804</td>\n      <td>Rosebank Mall</td>\n      <td>Licensed</td>\n      <td>Cnr Tyrwhitt and Cradock Avenue, Rosebank</td>\n      <td>Johannesburg</td>\n      <td>GT</td>\n      <td>ZA</td>\n      <td>2194</td>\n      <td>27873500159</td>\n      <td>GMT+000000 Africa/Johannesburg</td>\n      <td>28.04</td>\n      <td>-26.15</td>\n    </tr>\n    <tr>\n      <th>25598</th>\n      <td>Starbucks</td>\n      <td>47640-253809</td>\n      <td>Menlyn Maine</td>\n      <td>Licensed</td>\n      <td>Shop 61B, Central Square, Cnr Aramist &amp; Coroba...</td>\n      <td>Menlyn</td>\n      <td>GT</td>\n      <td>ZA</td>\n      <td>181</td>\n      <td>NaN</td>\n      <td>GMT+000000 Africa/Johannesburg</td>\n      <td>28.28</td>\n      <td>-25.79</td>\n    </tr>\n    <tr>\n      <th>25599</th>\n      <td>Starbucks</td>\n      <td>47609-253286</td>\n      <td>Mall of Africa</td>\n      <td>Licensed</td>\n      <td>Shop 2077, Upper Level, Waterfall City</td>\n      <td>Midrand</td>\n      <td>GT</td>\n      <td>ZA</td>\n      <td>1682</td>\n      <td>27873500215</td>\n      <td>GMT+000000 Africa/Johannesburg</td>\n      <td>28.11</td>\n      <td>-26.02</td>\n    </tr>\n  </tbody>\n</table>\n<p>25600 rows × 13 columns</p>\n</div>"
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 导入星巴克店的数据\n",
    "starbucks = pd.read_csv(\"directory.csv\")\n",
    "starbucks"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-22T11:25:35.905361700Z",
     "start_time": "2024-02-22T11:25:35.840185600Z"
    }
   },
   "id": "b3bfe5b611685bce",
   "execution_count": 8
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 3.2 进行分组聚合\n"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "b26681f1fab05a24"
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "# 按照国家分组，求出每个国家的星巴克零售店数量\n",
    "count = starbucks.groupby(['Country']).count()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-22T11:26:06.252892900Z",
     "start_time": "2024-02-22T11:26:06.239050Z"
    }
   },
   "id": "6b726394583ff448",
   "execution_count": 10
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "         Brand  Store Number  Store Name  Ownership Type  Street Address  \\\nCountry                                                                    \nAD           1             1           1               1               1   \nAE         144           144         144             144             144   \nAR         108           108         108             108             108   \nAT          18            18          18              18              18   \nAU          22            22          22              22              22   \n...        ...           ...         ...             ...             ...   \nTT           3             3           3               3               3   \nTW         394           394         394             394             394   \nUS       13608         13608       13608           13608           13608   \nVN          25            25          25              25              25   \nZA           3             3           3               3               3   \n\n          City  State/Province  Postcode  Phone Number  Timezone  Longitude  \\\nCountry                                                                       \nAD           1               1         1             1         1          1   \nAE         144             144        24            78       144        144   \nAR         108             108       100            29       108        108   \nAT          18              18        18            17        18         18   \nAU          22              22        22             0        22         22   \n...        ...             ...       ...           ...       ...        ...   \nTT           3               3         3             0         3          3   \nTW         394             394       365            39       394        394   \nUS       13608           13608     13607         13122     13608      13608   \nVN          25              25        25            23        25         25   \nZA           3               3         3             2         3          3   \n\n         Latitude  \nCountry            \nAD              1  \nAE            144  \nAR            108  \nAT             18  \nAU             22  \n...           ...  \nTT              3  \nTW            394  \nUS          13608  \nVN             25  \nZA              3  \n\n[73 rows x 12 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Brand</th>\n      <th>Store Number</th>\n      <th>Store Name</th>\n      <th>Ownership Type</th>\n      <th>Street Address</th>\n      <th>City</th>\n      <th>State/Province</th>\n      <th>Postcode</th>\n      <th>Phone Number</th>\n      <th>Timezone</th>\n      <th>Longitude</th>\n      <th>Latitude</th>\n    </tr>\n    <tr>\n      <th>Country</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>AD</th>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>AE</th>\n      <td>144</td>\n      <td>144</td>\n      <td>144</td>\n      <td>144</td>\n      <td>144</td>\n      <td>144</td>\n      <td>144</td>\n      <td>24</td>\n      <td>78</td>\n      <td>144</td>\n      <td>144</td>\n      <td>144</td>\n    </tr>\n    <tr>\n      <th>AR</th>\n      <td>108</td>\n      <td>108</td>\n      <td>108</td>\n      <td>108</td>\n      <td>108</td>\n      <td>108</td>\n      <td>108</td>\n      <td>100</td>\n      <td>29</td>\n      <td>108</td>\n      <td>108</td>\n      <td>108</td>\n    </tr>\n    <tr>\n      <th>AT</th>\n      <td>18</td>\n      <td>18</td>\n      <td>18</td>\n      <td>18</td>\n      <td>18</td>\n      <td>18</td>\n      <td>18</td>\n      <td>18</td>\n      <td>17</td>\n      <td>18</td>\n      <td>18</td>\n      <td>18</td>\n    </tr>\n    <tr>\n      <th>AU</th>\n      <td>22</td>\n      <td>22</td>\n      <td>22</td>\n      <td>22</td>\n      <td>22</td>\n      <td>22</td>\n      <td>22</td>\n      <td>22</td>\n      <td>0</td>\n      <td>22</td>\n      <td>22</td>\n      <td>22</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>TT</th>\n      <td>3</td>\n      <td>3</td>\n      <td>3</td>\n      <td>3</td>\n      <td>3</td>\n      <td>3</td>\n      <td>3</td>\n      <td>3</td>\n      <td>0</td>\n      <td>3</td>\n      <td>3</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>TW</th>\n      <td>394</td>\n      <td>394</td>\n      <td>394</td>\n      <td>394</td>\n      <td>394</td>\n      <td>394</td>\n      <td>394</td>\n      <td>365</td>\n      <td>39</td>\n      <td>394</td>\n      <td>394</td>\n      <td>394</td>\n    </tr>\n    <tr>\n      <th>US</th>\n      <td>13608</td>\n      <td>13608</td>\n      <td>13608</td>\n      <td>13608</td>\n      <td>13608</td>\n      <td>13608</td>\n      <td>13608</td>\n      <td>13607</td>\n      <td>13122</td>\n      <td>13608</td>\n      <td>13608</td>\n      <td>13608</td>\n    </tr>\n    <tr>\n      <th>VN</th>\n      <td>25</td>\n      <td>25</td>\n      <td>25</td>\n      <td>25</td>\n      <td>25</td>\n      <td>25</td>\n      <td>25</td>\n      <td>25</td>\n      <td>23</td>\n      <td>25</td>\n      <td>25</td>\n      <td>25</td>\n    </tr>\n    <tr>\n      <th>ZA</th>\n      <td>3</td>\n      <td>3</td>\n      <td>3</td>\n      <td>3</td>\n      <td>3</td>\n      <td>3</td>\n      <td>3</td>\n      <td>3</td>\n      <td>2</td>\n      <td>3</td>\n      <td>3</td>\n      <td>3</td>\n    </tr>\n  </tbody>\n</table>\n<p>73 rows × 12 columns</p>\n</div>"
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-22T11:26:08.285139100Z",
     "start_time": "2024-02-22T11:26:08.277275700Z"
    }
   },
   "id": "740505a4d2dce908",
   "execution_count": 11
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 2000x800 with 1 Axes>",
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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "count['Brand'].plot(kind='bar', figsize=(20, 8))\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-22T11:26:30.195224400Z",
     "start_time": "2024-02-22T11:26:29.937052700Z"
    }
   },
   "id": "dd4b7d9acb20ca2a",
   "execution_count": 13
  },
  {
   "cell_type": "markdown",
   "source": [
    "# 4 小结\n",
    "- groupby进行数据的分组【知道】\n",
    "- pandas中，抛开聚合谈分组，无意义- "
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "34d1432bccb3d122"
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
